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Asian Control Conference

作者： Haibao Tian Xiuxian Li Min Meng Zhen DONG Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China College of Electronics and Information Engineering Tongji University Shanghai China Suzhou SEEExTECH Co. Ltd SEEExTECH Suzhou China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper focuses on Online Federated Composite Optimization (OFCO) problem, where the loss function con-tains a non-smooth regularizer and the network environment is time-varying. This problem is prevalent in various real-world applications, spanning from wireless sensor networks to signal processing. To address the challenge posed by OFCO, we propose a novel federated learning algorithm, named FedOE, which draws inspiration from approximate composite mirror descent. Furthermore, FedOE incorporates a multi-kernel strategy to enhance accuracy and flexibility, emphasizing a comprehensive and effective solution to the OFCO problem. Through theoretical analysis, FedOE achieves a regularized regret on the order of O(VT) with the total number of rounds T. Finally, the numerical experiments validate the efficacy of the proposed algorithm.

关键词： Wireless sensor networks Accuracy Federated learning Signal processing algorithms Signal processing approximation algorithms Mirrors

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Fully Privacy-Preserving and Efficient Clustering Scheme based on Fully Homomorphic Encryption

Fully Privacy-Preserving and Efficient Clustering Scheme bas...

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IEEE International Conference on Communications (ICC)

作者： Mengyu Zhang Long Wang Xiaoping Zhang Yisong Wang Wenhou Sun Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Fully Homomorphic Encryption is a cryptographic scheme to prevent the privacy leakage of sensitive data. However, one challenge with FHE is the ciphertext comparison widely used in clustering, an important scheme used for data mining and analysis. In this paper, we create a series of ciphertext comparison functions by rewriting the comparison in HE-friendly operations and the Heaviside step function approximated by Chebyshev Polynomials. Furthermore, we solve the challenging ciphertext division through constructing a function whose root is the reciprocal of the divisor and applying Newton's method to ap-proximate the root. Then, we propose a fully privacy-preserving, effective and efficient clustering scheme based on our ciphertext comparison and division. Tests on various datasets show that our algorithm maintains nearly the same classification accuracy as vanilla k-means and significantly outperforms the baseline in terms of accuracy. We then optimize our scheme by batching over multiple records and multithreading, and the results show that our approach has a significant efficiency advantage. Compared with the state-of-the-art FHE-based privacy-preserving clustering scheme (SAC 2018), our algorithm is four orders of magnitude faster than theirs.

关键词： Data privacy Accuracy Multithreading Clustering algorithms Chebyshev approximation approximation algorithms Classification algorithms

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Dynamic Computing Power Wavelength Allocation Based on Deep Q-Learning for WDM-PON

Dynamic Computing Power Wavelength Allocation Based on Deep ...

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OptoElectronics and Communications Conference, OECC

作者： Wenbo Lin Tiankuo Yu Hui Yang Qiuyan Yao Chen Zhang Jie Zhang StateKey Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

This paper proposes a dynamic computing power wavelength allocation scheme based on deep Q-learning for WDM-PON, which can reduce spectrum fragmentation during massive computing power requests. Without introducing del... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Q-learning Processor scheduling Heuristic algorithms Computational modeling Wavelength division multiplexing Dynamic scheduling approximation algorithms Delays Resource management

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approximation algorithms for the Stochastic Lot-Sizing Problem with Order Lead Times

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OPERATIONS RESEARCH 2013年第3期61卷 593-602页

作者： Levi, Retsef Shi, Cong MIT Alfred P Sloan Sch Management Cambridge MA 02139 USA Univ Michigan Ann Arbor MI 48109 USA

We develop new algorithmic approaches to compute provably near-optimal policies for multiperiod stochastic lot-sizing inventory models with positive lead times, general demand distributions, and dynamic forecast updates. The policies that are developed have worst-case performance guarantees of 3 and typically perform very close to optimal in extensive computational experiments. The newly proposed algorithms employ a novel randomized decision rule. We believe that these new algorithmic and performance analysis techniques could be used in designing provably near-optimal randomized algorithms for other stochastic inventory control models and more generally in other multistage stochastic control problems.

关键词： inventory stochastic lot-sizing approximation algorithms randomized cost balancing

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Collaborative Multi-Agent Jamming Deceiving for UAV-assisted Wireless Communications

Collaborative Multi-Agent Jamming Deceiving for UAV-assisted...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Chen Zhang Zhenyu Xiao Lingfeng Zhang Guangyao Liu Wei Zhang Xiang-Gen Xia School of Electronic and Information Engineering Beihang University Beijing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney NSW Australia Department of Electrical and Computer Engineering University of Delaware Newark DE USA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

A reactive jamming attack, which performs spectrum jamming only during legal signal transmission based on the knowledge of user behaviors, poses a significant threat to wireless communications. In this paper, a novel deceiving approach is proposed for defending reactive jamming in unmanned aerial vehicle (UAV) assisted wireless communications. Specifically, the interaction of multiple legitimate users (LUs) and a malicious user (MU) is modelled as a Stackelberg game, where LUs are the leaders and MU is the follower. We first show that the equilibrium exists when the channel information is known. Then, we propose a multi-agent reinforcement learning (MARL) based method to address the reactive jamming. Finally, we perform simulations to demonstrate the superiority of our proposed method.

关键词： Wireless communication Reinforcement learning Games Autonomous aerial vehicles approximation algorithms Numerical models Resource management

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Matrix Profile XXXI: Motif Discovery Made Faster

Matrix Profile XXXI: Motif Discovery Made Faster

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IEEE International Conference on Big Knowledge (ICBK)

作者： Maryam Shahcheraghi Chin-Chia Michael Yeh Yan Zheng Junpeng Wang Zhongfang Zhuang Liang Wang Mahashweta Das Eamonn Keogh University of California Riverside Visa Research Foster City USA

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Approximately repeated subsequences in a longer time series, i.e., time series motifs, are important primitive in time series data mining. Motifs are used in dozens of downstream tasks, including classification, clustering, summarization, rule discovery, segmentation etc. Time series motif discovery is a notoriously computationally expensive task. Some motif discovery algorithms are fast in the best case, but in other datasets, even if both the data and motif lengths are held the same, both their time and space complexity can explode. The Matrix Profile has the nice property that its time and space complexity are independent of the data. Moreover, the Matrix Profile is fast enough for datasets with about one million datapoints, which covers a large fraction of user cases. However, there are situations where we may wish to consider datasets which are much larger. In this work, we introduce the first lower bound for the Matrix Profile and an algorithm that exploits that lower bound to allow orders of magnitude speed up for exact motif search on real-world datasets. We demonstrate the utility of our ideas with the largest and most ambitious motif discovery experiments ever attempted.

关键词： Lower bound Time series analysis Clustering algorithms Knowledge graphs Search problems approximation algorithms Complexity theory Classification algorithms Data mining

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Parallel approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation approximation algorithms

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Image Encryption Algorithm Based on Matrix Semi-Tensor Product in DWT Domain

Image Encryption Algorithm Based on Matrix Semi-Tensor Produ...

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Chinese Automation Congress (CAC)

作者： Jinwei Yu Junhai Zeng Man Li Wei Xie Langwen Zhang Yindong Lian China Telecom Research Institute Guangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China Southern Power Grid Supply Chain Technology (Guangdong) Co. Ltd Guangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper proposed an image encryption method based on matrix semi-tensor product (STP) and discrete wavelet transform (DWT). Firstly, a two-dimensional chaotic system is utilized to produce random sequences. Secondly, the plaintext image is converted to wavelet coefficients based on discrete wavelet transform. Next, the wavelet coefficients are encrypted based on STP and chaotic sequences. Then the encrypted information in DWT domain are converted into space domain via inverse discrete wavelet transform (IDWT). At last, the image is diffused to obtain the encrypted image. The proposed method is highly secure against statistical attacks according to the security analysis, as the information entropy reaches 7.9971.

关键词： Resistance Wavelet domain Statistical analysis approximation algorithms Discrete wavelet transforms Encryption Matrix converters Information entropy Wavelet coefficients Random sequences

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Directed Buy-at-Bulk Spanners

arXiv

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arXiv 2024年

作者： Grigorescu, Elena Kumar, Nithish Lin, Young-San Purdue University United States Melbourne Business School Australia

We present a framework that unifies directed buy-at-bulk network design and directed spanner problems, namely, buy-at-bulk spanners. The goal is to find a minimum-cost routing solution for network design problems that capture economies at scale, while satisfying demands and distance constraints for terminal pairs. A more restricted version of this problem was shown to be O(2log1−Ε n)-hard to approximate, where n is the number of vertices, under a standard complexity assumption, due to Elkin and Peleg (Theory of Computing Systems, 2007). Our results for buy-at-bulk spanners are the following. 1. When the edge lengths are integral with magnitude polynomial in n we present: (a) An Õ(n4/5+Ε)-approximation polynomial-time randomized algorithm for uniform demands. (b) An Õ(k1/2+Ε)-approximation polynomial-time randomized algorithm for general demands, where k is the number of terminal pairs. This can be improved to an Õ(kΕ)-approximation algorithm for the single-source problem. 2. When the edge lengths are rational and well-conditioned, we present an Õ(k1/2+Ε)-approximation polynomial-time randomized algorithm which may slightly violate the distance constraints. The result can be improved to an Õ(kΕ)-approximation algorithm for the single-source problem. To the best of our knowledge, these are the first sublinear factor approximation algorithms for directed buy-at-bulk spanners. Furthermore, these results hold even when we allow the edge lengths to be negative, unlike the previous literature for spanners. Our approximation ratios match the state-of-the-art ratios in special cases, namely, buy-at-bulk network design by Antonakopoulos (WAOA, 2010) and weighted spanners by Grigorescu, Kumar, and Lin (APPROX 2023). Our results are based on approximation algorithms for the following two problems that are of independent interest: minimum-density distance-constrained junction trees and resource-constrained shortest path with negative consumption. In the minimum-density distance-

关键词： approximation algorithms

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Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite Networks

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Proceedings of the ACM on Management of Data 2023年第4期1卷 1-26页

作者： Fangyuan Zhang Dechuang Chen Sibo Wang Yin Yang Junhao Gan The Chinese University of Hong Kong Hong Kong Hong Kong Hamad Bin Khalifa University Doha Qatar University of Melbourne Melbourne VIC Australia

A bipartite graph is a graph that consists of two disjoint sets of vertices and only edges between vertices from different vertex sets. In this paper, we study the counting problems of two common types of em motifs in bipartite graphs: (i) butterflies (2x2 bicliques) and (ii) bi-triangles (length-6 cycles). Unlike most of the existing algorithms that aim to obtain exact counts, our goal is to obtain precise enough estimations of these counts in bipartite graphs, as such estimations are already sufficient and of great usefulness in various applications. While there exist approximate algorithms for butterfly counting, these algorithms are mainly based on the techniques designed for general graphs, and hence, they are less effective on bipartite graphs. Not to mention that there is still a lack of study on approximate bi-triangle counting. Motivated by this, we first propose a novel butterfly counting algorithm, called one-sided weighted sampling, which is tailored for bipartite graphs. The basic idea of this algorithm is to estimate the total butterfly count with the number of butterflies containing two randomly sampled vertices from the same side of the two vertex sets. We prove that our estimation is unbiased, and our technique can be further extended (non-trivially) for bi-triangle count estimation. Theoretical analyses under a power-law random bipartite graph model and extensive experiments on multiple large real datasets demonstrate that our proposed approximate counting algorithms can reach high accuracy, yet achieve up to three orders (resp. four orders) of magnitude speed-up over the state-of-the-art exact butterfly (resp. bi-triangle) counting algorithms. Additionally, we present an approximate clustering coefficient estimation framework for bipartite graphs, which shows a similar speed-up over the exact solutions with less than 1% relative error.

关键词： approximation algorithms graph algorithms sampling

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